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1.
AIMS Biophysics ; 8(4):346-371, 2021.
Article in English | Scopus | ID: covidwho-1964164

ABSTRACT

The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

2.
Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021 ; 3159:1204-1209, 2021.
Article in English | Scopus | ID: covidwho-1957778

ABSTRACT

In the advent of Natural Language Processing, what finds itself in much use is analysis. This research paper finds itself in reference to the same that enables it in analysing sentiments of a text. The tasks that were covered in working with NLP includes – firstly, differentiating tweets on the basis of claims and facts, and secondly to create an effective classifier that finds out if a tweet is anti-covid vaccine, pro-covid vaccine or neutral. The beauty of our paper resides in the fact, that we have hit high end accuracies without using hefty algorithms, namely 93% for the first task using Random Forest and 45.4% for the second task using BERT’s Algorithm. Our accuracies are the best among all the teams working on the same tasks, which deepens the effect that this paper resonates. The details of the IRMiDis 2021 data challenge have been discussed elaborately here, and we hope our paper marks its significance by virtue of its own merit. © 2021 Copyright for this paper by its authors.

3.
Rheumatology (United Kingdom) ; 61(SUPPL 1):i98, 2022.
Article in English | EMBASE | ID: covidwho-1868409

ABSTRACT

Background/Aims Chilblain-like lesions (perniosis) have been reported frequently during COVID-19 pandemic in children and adolescents with no history of exposure to cold temperatures or underlying autoimmune conditions. Patients with these skin changes reported mild COVID-19 symptoms or previous contact with confirmed COVID-19 cases before they became symptomatic. In the majority of cases, a causal relationship between SARS-CoV-2 infection and chilblain-like lesion has not been proven. Methods Retrospective review of patients with chilblain-like lesions, possibly secondary to SARS-CoV-2 infection, presenting to a tertiary Adolescent Rheumatology service between January and August 2021. Results We identified five, male, adolescent patients (mean age, 16 years old) who presented with new onset of chilblain-like lesions affecting fingers, toes and heels in December 2020, which coincided with the peak of second wave of COVID-19 infection. One month prior to skin changes occurrence, 3 out of 5 patients experienced mild respiratory COVID-19-like symptoms and the rest of the patients were asymptomatic but were in contact with COVID-19 positive cases following outbreaks in schools. 1 of 3 symptomatic patients had a positive COVID-19 PCR test prior to skin manifestations. 2 out of 4 patients with heel lesions had deep, full thickness skin loss heel ulcers and 2 of 5 patients had superficially ulcerated lesions on a finger and toes, respectively, resulting in inability to attend school. None of the patients had any other symptoms or signs to suggest an underlying autoimmune connective tissue disorder. Demographics, clinical features and serological data are summarised in Table 1. One patient underwent a biopsy of heel ulcer which was histologically consistent with perniosis. In two patients (40%) chilblain like lesions resolved spontaneously within 2 months. Three patients (60%), with progressive ulcerated lesions, required various combinations of treatments with aspirin, calcium channel blockers (nifedipine), topical or oral steroids and hydroxychloroquine with complete resolution of symptoms within 6 months. Conclusion Chilblain-like lesions, including heel involvement associated with mildly symptomatic COVID-19 infection, have been reported before. Our mini-case series raises awareness of ulcerating chilblain like lesions possibly secondary to COVID-19 in adolescent patients, which require early recognition and instigation of treatment leading to better patient's outcomes.

4.
1st International Conference of IoT and its Applications, ICIA2020 ; 825:293-301, 2022.
Article in English | Scopus | ID: covidwho-1750632

ABSTRACT

The unprecedented rise and spread of the pandemic in form of nCOVID-19 has really raised high concerns in the socioeconomic front. The usual diagnosis is made by an RT-PCR test, which is highly specific can incorrectly identify some nCOVID-19 individuals to cause a serious compromise in overall accuracy. Since the drug application in its full swing is still some months away, hence, the need of the hour is to find a more accurate technique which can be used by health care centers having basic point of care facilities. The increase in the number of cases in India and lack of test kits in some of the less known diagnostic centers has added more concerns to the increasing problems. Additionally, the test kits incur a significant cost making it less affordable to some of the diagnostic centers. Hence, this research group in this article has proposed an algorithm centered around the concept of Internet of Things, a dual deep learning based algorithm, and collating the decision by a strong decision fusion technique. The objective of the algorithm is to detect and isolate the nCOVID-19 subjects in a cost-effective way to keep a check on the spread. This pandemic detection and isolation technique (PANDIT) is based on two different radiography image technology and uses a state-of-the-art deep learning algorithm for the purpose. The radiography technique has long been the most acceptable technique for cases related to pneumonia. The group has developed the algorithm based on X-ray and CT scan as its training data. The novelty of this paper is best described by a multi-fold methodology. Firstly, the significance of radiography imaging for detecting and identification of COVID-19 subjects. A simple connected value chain driven by Internet of Things (IoT) would enable the isolation process in an efficient and accelerated manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
2021 Winter Simulation Conference, WSC 2021 ; 2021-December, 2021.
Article in English | Scopus | ID: covidwho-1746027

ABSTRACT

Collision-free or contact-free routing through connected networks has been actively studied in the industrial automation and manufacturing context. Contact-free routing of personnel through connected networks (e.g., factories, retail warehouses) may also be required in the COVID-19 context. In this context, we present an optimization framework for identifying routes through a connected network that eliminate or minimize contacts between randomly arriving agents needing to visit a subset of nodes in the network in minimal time. We simulate the agent arrival and network traversal process, and introduce stochasticity in travel speeds, node dwell times, and compliance with assigned routes. We present two optimization formulations for generating optimal routes-no-contact and minimal-contact-on a real-time basis for each agent arriving to the network given the route information of other agents already in the network. We generate results for the time-average number of contacts and normalized time spent in the network. © 2021 IEEE.

6.
International Journal of Statistics in Medical Research ; 10:146-160, 2021.
Article in English | Scopus | ID: covidwho-1591784

ABSTRACT

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem. © 2021 Lifescience Global. All Rights Reserved.

7.
Immunity Boosting Functional Foods to Combat COVID-19 ; : 169-176, 2021.
Article in English | Scopus | ID: covidwho-1519225

ABSTRACT

The integral relationship between diet and health is well established now these days;therefore the proper utilization of nutrient-rich residues from food industries for designing the value added products, is the basic target for the modern food industry. Whey, is an important dairy industry waste and has been categorized to be admirable nourishment with an assortment of bioactive components. Whey protein comprises 20% of total milk protein provides a biological activity that surpasses the properties of superior quality amino acids. These proteins are one of the very few ingredients shown to modulate immune function in both in-vitro cell culture studies and in-vivo animal models. Whey proteins have proved beneficial against a wide spectrum of life threating diseases like cancer, diabetes, hypertension, obesity etc. This protein is also very useful for the recovery of exercise-injuries or skin diseases from radiations. Apart from health beneficial effects, it has several functional activities like fat replacer and emulsifier. This valuable protein is proving to be an immune-nutrient and its dietary interference to tackle life threating disease like cancer or viral diseases like COVID-19. © 2021, Narendra Publishing House, Delhi, India.

8.
1st International Conference on Advances in Medical Physics and Healthcare Engineering, AMPHE 2020 ; : 393-404, 2021.
Article in English | Scopus | ID: covidwho-1353686

ABSTRACT

The entire world faced locked down scenario due to the outbreak of nCOVID-19 corona virus outbreak. The fast and relentless spread nCOVID-19 has basically segmented the populace only into three subclasses, namely susceptible, infected, and recovered compartments. Adapting the classical SIR-type epidemic modeling framework, the direct person-to-person contact transmission is taken as the direct route of transmission of nCOVID-19 pandemic. In this research, the authors have developed two models of the nation-wide trends of the outburst of the nCOVID-19 infection using an SIR model and also an ARIMA model. They have studied the quantile plots, regression residual plots and R pair plots of the dataset by simple supervised machine learning algorithms. This study compares both models and higher correlation of the developed models with reality which suggests the extent of accuracy of these models. The study also suggested some possible way-out to get rid of this situation by providing a trade-off between ‘flattening of the curve’ as well as less economic turbulence. The projections are intended to provide an action plan for the socioeconomic counter measures to alleviate COVID-19 in India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

9.
Int. Conf. Adv. Comput. Innov. Technol. Eng., ICACITE ; : 158-164, 2021.
Article in English | Scopus | ID: covidwho-1218993

ABSTRACT

The nCOVID-19 has wreaked our normal lifestyle and has forced us to adopt the protocols under the new normal regime. The conventional diagnostic approach is expected to change in the process as well. Our research group is proposing an aid to such diagnostic approach. The group has proposed in this article the power of using two such diagnostic measures that has been the pivot for many diagnoses to the doctors. The art of using natural language processing based symptomatic measure in combination with a machine learning based approach based on medical vitals can collectively reduce the error percentage of detection. The approach proposed in this article is a first of its kind and the authors have achieved acceptable results on the accuracy front. The other reason of proposing such a technique is the way a fusion algorithm can arrive at the right results from two parallel algorithms doing the same task. Another objective of the group was to provide a valuable opinion to the doctor in form of such an architecture. The proposed architecture can be used at any point of care facilities without any requirement of escalation of the existing amenities. © 2021 IEEE.

10.
Occupational Medicine ; 30:30, 2021.
Article in English | MEDLINE | ID: covidwho-1209653

ABSTRACT

BACKGROUND: Work-related stress, anxiety and depression (WRSAD) are common, overlapping mental health problems burdened with major medical, occupational, institutional and societal implications. Current occupational health (OH) management of WRSAD is based on clinical and managerial guidelines and individual risk assessment. AIMS: The study sought to identify patterns of OH advice in WRSAD and the relationships between the OH advice, available evidence, experience and expertise of the OH doctors (OHDs). METHODS: A retrospective cross-sectional analysis of 101 first-time OH consultations for WRSAD by nine OHDs. RESULTS: The three most common OH interventions included follow-up OH consultations, adjusted duties and referrals for counselling. All OHDs preferred a light-touch approach but the less experienced and qualified OHDs were more proactive and prescriptive. CONCLUSIONS: In the absence of a specific occupational medical guideline for the management of WRSAD, the OH interventions may be guided by clinical guidelines, individual risk assessment, the client's circumstances or the experience, expertise and preferences of the OHDs. In the study group, OH interventions were under-utilized and not consistently applied. Our findings support the argument for OH guideline for WRSAD to improve the consistency and effectiveness of OH interventions. This is important given the scale of the problem and the recent increase in WRSAD during the COVID-19 pandemic.

11.
Industrial and Engineering Chemistry Research ; 2021.
Article in English | Scopus | ID: covidwho-1104417

ABSTRACT

Since the starting of the year 2020, the whole world is facing a challenge due to an outbreak of an unprecedented COVID-19 pandemic owing to a novel coronavirus. Here, a modified susceptible-infected-recovered-dead model has been used to analyze the time series data of the pandemic for five countries. It is established that the present model is capable of simultaneously explaining the temporal evolution of active-infected, recovered, and dead population of all these five countries. The key parameters governing the temporal evolution of the spread of this pandemic are estimated and compared. © 2021 American Chemical Society.

12.
IEEE Solid-State Circuits Magazine ; 12(4):33-47, 2020.
Article in English | Scopus | ID: covidwho-960724

ABSTRACT

The world has been in combat with COVID-19 since December 2019. The United States has been paralyzed, with the highest number of cases and more deaths than any other country. However, the disease has devastated almost every nation across the globe, from the Far East to Europe, Latin America, Russia, and India. © 2009-2012 IEEE.

13.
Biological Rhythm Research ; 2020.
Article in English | EMBASE | ID: covidwho-883016

ABSTRACT

Lockdown is an important measure that has been globally adopted to reduce the spread of the contagious disease caused by SARS CoV-2. The imposed schedule and confinement led to extensive use of digital media and rise in sedentary activity drastically. The escalated duration of screen exposure causes disruption in sleep behavior. An online survey was conducted to comprehend the effect of lockdown on sleep behavior and screen exposure time on school children. Screen exposure time involved with various electronic gadgets was also analyzed. It was observed that the social jet lag and sleep debt were significantly less during lockdown than before it. Inertia during lockdown significantly increased. The difference between screen exposure time on weekdays before lockdown and weekends during lockdown was identified to be the highest. Three clusters based on sleep behavior and duration of screen time were identified of which Cluster 2 revealed simultaneous existence of high sleep duration and screen time. These baseline data on sleep parameters and duration of exposure to the screen will help us in devising approaches to mitigate the evident disruption this unprecedented phase has brought about.

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